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Examining the determinants of the count of customer reviews in peer-to-peer home-sharing platforms using clustering and count regression techniques
Decision Support Systems ( IF 6.7 ) Pub Date : 2020-05-22 , DOI: 10.1016/j.dss.2020.113324
Baidyanath Biswas , Pooja Sengupta , Dwaipayan Chatterjee

The sharing economy has experienced massive growth in the short-term shared-home rental industry. However, few studies have investigated the determinants of the number of customer reviews received by these shared-homes. To fill this gap, we were motivated to propose an analytical framework that identified these determinants, both explicit and implicit. We applied Poisson, Quasi-Poisson, and Negative Binomial regressions with a dataset consisting of Airbnb properties from ten different cities worldwide, while successful bookings were proxied by the count of customer reviews posted by guests. We performed a cluster analysis based on the properties to generate homogeneous “cluster cities” and performed the regressions separately for each cluster. Among host-generated features, superhost, host duration, bedrooms, and amenities became significant. Among user-generated features, overall review scores and negative sentiments were significant. We also found that the “superhost” badge moderated the effects of host-generated content on the count of customer reviews. Consequently, guests paid a higher “price per night” for “superhost” properties, while they overlooked crucial attributes such as “website features.” Through these novel “cluster-specific” recommendations, our study extends the existing theories and contributes to the literature of decision analytics and tourism management. Finally, we performed a sensitivity analysis to check for the timeliness and robustness of these determinants.



中文翻译:

使用聚类和计数回归技术检查对等房屋共享平台中客户评论数量的决定因素

共享经济在短期共享住房租赁行业中经历了巨大的增长。但是,很少有研究调查这些共享房屋收到的​​顾客评论数量的决定因素。为了填补这一空白,我们有动机提出了一个分析框架,该框架可以确定这些决定因素,包括显性的和隐性的。我们将Poisson,Quasi-Poisson和Negative Binomial回归应用于包含来自全球十个不同城市的Airbnb属性的数据集,而成功的预订则取决于客人发表的客户评论数量。我们基于属性进行聚类分析以生成同质的“聚类城市”,并对每个聚类分别进行回归。在主机生成的功能中,超级主机,主机时间卧室便利设施变得很重要。在用户生成的功能中,总体评论评分负面情绪非常重要。我们还发现,“超级主机”徽章减轻了主机生成的内容对客户评论数量的影响。因此,来宾为“超级主机”属性支付了更高的“每晚价格”,而忽略了诸如“网站功能”之类的关键属性。通过这些新颖的“特定于集群的”建议,我们的研究扩展了现有理论,并为决策分析和旅游管理的文献做出了贡献。最后,我们进行了敏感性分析,以检查这些决定因素的及时性和稳健性。

更新日期:2020-06-29
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